def cond_probs(self, y_set, gamma_set):
"""
Given the set of gamma variables, outputs the set of
probabilities p(y_t | y_{t-1}, ... , y_1, gamma_{t-1}, ... , gamma_1)
"""
# Note (HUGO): this function should probably be implemented in C
# to make it much faster, since it requires for loops.
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
cond_probs = []
map_probs = []
laplace_probs = []
y_pred = []
z_n_z_n_post_sum = zeros((d_z, d_z))
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_z2 = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z, d_z))
mat_d_z_d_z2 = zeros((d_z, d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z, d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y, d_y))
A_gamma = zeros((d_z, d_z))
E_gamma = zeros((d_z, d_z))
K = zeros((d_z, d_y))
KC = zeros((d_z, d_z))
J = zeros((d_z, d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y, d_y), order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y, d_z), order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z, d_z),
order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z, d_z), order='fortran')
pivots_d_y = zeros((d_y), dtype='i', order='fortran')
pivots_d_z = zeros((d_z), dtype='i', order='fortran')
z_n_z_n_post = zeros((d_z, d_z))
next_z_n_z_n_post = zeros((d_z, d_z))
log_det_diff2_log_gamma = 0
for y_t, gamma_t in zip(y_set, gamma_set):
T = len(y_t)
cond_probs_t = zeros(T)
map_probs_t = zeros(T)
laplace_probs_t = zeros(T)
y_pred_t = zeros((T, d_y))
mu_kalman_t = zeros((T, d_z)) # Filtering mus
E_kalman_t = zeros((T, d_z, d_z)) # Filtering Es
mu_post_t = zeros((T, d_z))
E_post_t = zeros((T, d_z, d_z))
P_t = zeros((T - 1, d_z, d_z))
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
multiply(C.T, reshape(gamma_t[0], (-1, 1)), mat_times_C_trans)
pred[:] = 0
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y, Bf_d_y_d_z,
pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[0])
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
multiply(mat_d_z_d_z, gamma_t[0], E_kalman_t[0])
cond_probs_t[0] = self.multivariate_norm_log_pdf(
y_t[0], pred, cov_pred)
y_pred_t[0] = pred
# from n=1 to T-1
for n in xrange(T - 1):
divide(1., E, vec_d_z)
divide(1., gamma_t[n + 1], vec_d_z2)
vec_d_z += vec_d_z2
divide(1., vec_d_z, vec_d_z2)
setdiag(E_gamma, vec_d_z2)
divide(E, gamma_t[n + 1], vec_d_z)
vec_d_z += 1
divide(A, reshape(vec_d_z, (-1, 1)), A_gamma)
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n], A_gamma.T, mat_d_z_d_z)
product_matrix_matrix(A_gamma, mat_d_z_d_z, P_tn)
P_tn += E_gamma
product_matrix_vector(A_gamma, mu_kalman_t[n], A_times_prev_mu)
product_matrix_matrix(P_tn, C.T, mat_times_C_trans)
product_matrix_vector(C, A_times_prev_mu, pred)
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y,
Bf_d_y_d_z, pivots_d_y)
vec_d_y[:] = y_t[n + 1]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[n + 1])
mu_kalman_t[n + 1] += A_times_prev_mu
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z, P_tn, mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n + 1] = mat_d_z_d_z2
E_kalman_t[n + 1] += mat_d_z_d_z2.T
E_kalman_t[n + 1] /= 2
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1], mu_post_t[-1], z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
cond_probs_t[n + 1] = self.multivariate_norm_log_pdf(
y_t[n + 1], pred, cov_pred)
y_pred_t[n + 1] = pred
#print y_t, y_pred_t
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T - 2, -1, -1):
next_z_n_z_n_post[:] = z_n_z_n_post
divide(E, gamma_t[n + 1], vec_d_z)
vec_d_z += 1
divide(A, reshape(vec_d_z, (-1, 1)), A_gamma)
P_tn = P_t[n]
solve(P_tn.T, A_gamma, mat_d_z_d_z, Af_d_z_d_z, Bf_d_z_d_z,
pivots_d_z)
product_matrix_matrix(E_kalman_t[n], mat_d_z_d_z.T, J)
product_matrix_vector(A_gamma, mu_kalman_t[n], vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n + 1]
product_matrix_vector(J, vec_d_z, mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n + 1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z, J.T, mat_d_z_d_z2)
product_matrix_matrix(J, mat_d_z_d_z2, mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
outer(mu_post_t[n], mu_post_t[n], z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
dummy = self.compute_gamma(A, E, z_n_z_n_post,
next_z_n_z_n_post, gamma_t[n + 1])
log_prior_gamma = self.log_prior_gamma(gamma_t[n + 1])
#print log_prior_gamma
log_prior_log_gamma = self.log_prior_log_gamma(gamma_t[n + 1])
log_det_diff2_log_gamma = self.log_det_diff2_log_gamma(
A, E, z_n_z_n_post, next_z_n_z_n_post, gamma_t[n + 1])
map_probs_t[n + 1] = cond_probs_t[n + 1] + log_prior_gamma
laplace_probs_t[
n +
1] = cond_probs_t[n + 1] + log_prior_log_gamma + d_z * log(
2 * pi) / 2 - 0.5 * log_det_diff2_log_gamma
gamma_t[0] = (diag(z_n_z_n_post) + 2 * self.gamma_prior_beta) / (
2 * self.gamma_prior_alpha + 3)
log_prior_gamma = self.log_prior_gamma(gamma_t[0])
log_prior_log_gamma = self.log_prior_log_gamma(gamma_t[0])
log_det_diff2_log_gamma = sum(
(z_n_z_n_post / 2 + self.gamma_prior_beta) / gamma_t[0])
map_probs_t[0] = cond_probs_t[0] + log_prior_gamma
laplace_probs_t[
0] = cond_probs_t[0] + log_prior_log_gamma + d_z * log(
2 * pi) / 2 - 0.5 * log_det_diff2_log_gamma
cond_probs += [cond_probs_t]
map_probs += [map_probs_t]
laplace_probs += [laplace_probs_t]
y_pred += [y_pred_t]
return cond_probs, map_probs, laplace_probs, y_pred
def cond_probs(self,y_set,gamma_set):
"""
Given the set of gamma variables, outputs the set of
probabilities p(y_t | y_{t-1}, ... , y_1, gamma_{t-1}, ... , gamma_1)
"""
# Note (HUGO): this function should probably be implemented in C
# to make it much faster, since it requires for loops.
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
cond_probs = []
map_probs = []
laplace_probs = []
y_pred = []
z_n_z_n_post_sum = zeros((d_z,d_z))
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_z2 = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z,d_z))
mat_d_z_d_z2 = zeros((d_z,d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z,d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y,d_y))
A_gamma = zeros((d_z,d_z))
E_gamma = zeros((d_z,d_z))
K = zeros((d_z,d_y))
KC = zeros((d_z,d_z))
J = zeros((d_z,d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y,d_y),order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y,d_z),order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z,d_z),order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z,d_z),order='fortran')
pivots_d_y = zeros((d_y),dtype='i',order='fortran')
pivots_d_z = zeros((d_z),dtype='i',order='fortran')
z_n_z_n_post = zeros((d_z,d_z))
next_z_n_z_n_post = zeros((d_z,d_z))
log_det_diff2_log_gamma = 0
for y_t,gamma_t in zip(y_set,gamma_set):
T = len(y_t)
cond_probs_t = zeros(T)
map_probs_t = zeros(T)
laplace_probs_t = zeros(T)
y_pred_t = zeros((T,d_y))
mu_kalman_t = zeros((T,d_z)) # Filtering mus
E_kalman_t = zeros((T,d_z,d_z)) # Filtering Es
mu_post_t = zeros((T,d_z))
E_post_t = zeros((T,d_z,d_z))
P_t = zeros((T-1,d_z,d_z))
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
multiply(C.T,reshape(gamma_t[0],(-1,1)),mat_times_C_trans)
pred[:] = 0
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[0])
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
multiply(mat_d_z_d_z,gamma_t[0],E_kalman_t[0])
cond_probs_t[0] = self.multivariate_norm_log_pdf(y_t[0],pred,cov_pred)
y_pred_t[0] = pred
# from n=1 to T-1
for n in xrange(T-1):
divide(1.,E,vec_d_z)
divide(1.,gamma_t[n+1],vec_d_z2)
vec_d_z += vec_d_z2
divide(1.,vec_d_z,vec_d_z2)
setdiag(E_gamma,vec_d_z2)
divide(E,gamma_t[n+1],vec_d_z)
vec_d_z += 1
divide(A,reshape(vec_d_z,(-1,1)),A_gamma)
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n],A_gamma.T,mat_d_z_d_z)
product_matrix_matrix(A_gamma,mat_d_z_d_z,P_tn)
P_tn += E_gamma
product_matrix_vector(A_gamma,mu_kalman_t[n],A_times_prev_mu)
product_matrix_matrix(P_tn,C.T,mat_times_C_trans)
product_matrix_vector(C,A_times_prev_mu,pred)
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[n+1]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[n+1])
mu_kalman_t[n+1] += A_times_prev_mu
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z,P_tn,mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n+1] = mat_d_z_d_z2
E_kalman_t[n+1] += mat_d_z_d_z2.T
E_kalman_t[n+1] /= 2
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1],mu_post_t[-1],z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
cond_probs_t[n+1] = self.multivariate_norm_log_pdf(y_t[n+1],pred,cov_pred)
y_pred_t[n+1] = pred
#print y_t, y_pred_t
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T-2,-1,-1):
next_z_n_z_n_post[:] = z_n_z_n_post
divide(E,gamma_t[n+1],vec_d_z)
vec_d_z += 1
divide(A,reshape(vec_d_z,(-1,1)),A_gamma)
P_tn = P_t[n]
solve(P_tn.T,A_gamma,mat_d_z_d_z,Af_d_z_d_z,Bf_d_z_d_z,pivots_d_z)
product_matrix_matrix(E_kalman_t[n],mat_d_z_d_z.T,J)
product_matrix_vector(A_gamma,mu_kalman_t[n],vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n+1]
product_matrix_vector(J,vec_d_z,mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n+1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z,J.T,mat_d_z_d_z2)
product_matrix_matrix(J,mat_d_z_d_z2,mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
outer(mu_post_t[n],mu_post_t[n],z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
dummy = self.compute_gamma(A,E,z_n_z_n_post,next_z_n_z_n_post,gamma_t[n+1])
log_prior_gamma = self.log_prior_gamma(gamma_t[n+1])
#print log_prior_gamma
log_prior_log_gamma = self.log_prior_log_gamma(gamma_t[n+1])
log_det_diff2_log_gamma = self.log_det_diff2_log_gamma(A,E,z_n_z_n_post,next_z_n_z_n_post,gamma_t[n+1])
map_probs_t[n+1] = cond_probs_t[n+1]+log_prior_gamma
laplace_probs_t[n+1] = cond_probs_t[n+1]+log_prior_log_gamma+d_z*log(2*pi)/2-0.5*log_det_diff2_log_gamma
gamma_t[0] = (diag(z_n_z_n_post)+2*self.gamma_prior_beta)/(2*self.gamma_prior_alpha+3)
log_prior_gamma = self.log_prior_gamma(gamma_t[0])
log_prior_log_gamma = self.log_prior_log_gamma(gamma_t[0])
log_det_diff2_log_gamma = sum((z_n_z_n_post/2+self.gamma_prior_beta)/gamma_t[0])
map_probs_t[0] = cond_probs_t[0]+log_prior_gamma
laplace_probs_t[0] = cond_probs_t[0]+log_prior_log_gamma+d_z*log(2*pi)/2-0.5*log_det_diff2_log_gamma
cond_probs += [cond_probs_t]
map_probs += [map_probs_t]
laplace_probs += [laplace_probs_t]
y_pred += [y_pred_t]
return cond_probs, map_probs, laplace_probs, y_pred
def EM_step(self, y_set, gamma_set, training=False, return_mu_post=False):
"""
Computes the posterior statistics and outputs the M step
estimates of the parameters.
Also outputs the non-parametric, sparsity inducing variances gamma_t.
Optionally, can output the posterior means of the latent state variables.
"""
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
#V_zero = self.V_zero
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
# Variables for estimating new parameters
A_new = zeros((d_z, d_z))
C_new = zeros((d_y, d_z))
z_n_z_n_1_post_sum = zeros((d_z, d_z))
z_n_z_n_post_sum = zeros((d_z, d_z))
A_new_denums = zeros((d_z, d_z, d_z))
y_n_z_n_post_sum = zeros((d_y, d_z))
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_z2 = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z, d_z))
mat_d_z_d_z2 = zeros((d_z, d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z, d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y, d_y))
A_gamma = zeros((d_z, d_z))
E_gamma = zeros((d_z, d_z))
K = zeros((d_z, d_y))
KC = zeros((d_z, d_z))
J = zeros((d_z, d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y, d_y), order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y, d_z), order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z, d_z),
order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z, d_z), order='fortran')
pivots_d_y = zeros((d_y), dtype='i', order='fortran')
pivots_d_z = zeros((d_z), dtype='i', order='fortran')
z_n_z_n_1_post = zeros((d_z, d_z))
z_n_z_n_post = zeros((d_z, d_z))
weighted_z_n_z_n_post = zeros((d_z, d_z, d_z))
next_z_n_z_n_post = zeros((d_z, d_z))
y_n_z_n_post = zeros((d_y, d_z))
if training == True:
max_Esteps = self.max_Esteps
last_Esteps = self.last_Esteps
else:
max_Esteps = self.max_test_Esteps
last_Esteps = self.max_test_Esteps
Esteps = 0
have_A_denum = False
get_A_denum = False
finished = False
while not finished:
T_sum = 0
gamma_mean_diff = 0
z_n_z_n_1_post_sum[:] = 0
z_n_z_n_post_sum[:] = 0
y_n_z_n_post_sum[:] = 0
A_new_denums[:] = 0
Esteps += 1
if Esteps == max_Esteps:
get_A_denum = True
finished = True
elif Esteps >= last_Esteps:
get_A_denum = True
if return_mu_post:
mu_post = []
for y_t, gamma_t in zip(y_set, gamma_set):
T = len(y_t)
T_sum += T
mu_kalman_t = zeros((T, d_z)) # Filtering mus
E_kalman_t = zeros((T, d_z, d_z)) # Filtering Es
mu_post_t = zeros((T, d_z))
E_post_t = zeros((T, d_z, d_z))
P_t = zeros((T - 1, d_z, d_z))
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
multiply(C.T, reshape(gamma_t[0], (-1, 1)), mat_times_C_trans)
pred[:] = 0
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y,
Bf_d_y_d_z, pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[0])
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
multiply(mat_d_z_d_z, gamma_t[0], E_kalman_t[0])
# from n=1 to T-1
for n in xrange(T - 1):
divide(1., E, vec_d_z)
divide(1., gamma_t[n + 1], vec_d_z2)
vec_d_z += vec_d_z2
divide(1., vec_d_z, vec_d_z2)
setdiag(E_gamma, vec_d_z2)
divide(E, gamma_t[n + 1], vec_d_z)
vec_d_z += 1
divide(A, reshape(vec_d_z, (-1, 1)), A_gamma)
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n], A_gamma.T,
mat_d_z_d_z)
product_matrix_matrix(A_gamma, mat_d_z_d_z, P_tn)
P_tn += E_gamma
product_matrix_vector(A_gamma, mu_kalman_t[n],
A_times_prev_mu)
product_matrix_matrix(P_tn, C.T, mat_times_C_trans)
product_matrix_vector(C, A_times_prev_mu, pred)
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y,
Bf_d_y_d_z, pivots_d_y)
vec_d_y[:] = y_t[n + 1]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[n + 1])
mu_kalman_t[n + 1] += A_times_prev_mu
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z, P_tn, mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n + 1] = mat_d_z_d_z2
E_kalman_t[n + 1] += mat_d_z_d_z2.T
E_kalman_t[n + 1] /= 2
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1], mu_post_t[-1], z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
outer(y_t[-1], mu_post_t[-1], y_n_z_n_post)
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T - 2, -1, -1):
next_z_n_z_n_post[:] = z_n_z_n_post
divide(E, gamma_t[n + 1], vec_d_z)
vec_d_z += 1
divide(A, reshape(vec_d_z, (-1, 1)), A_gamma)
P_tn = P_t[n]
solve(P_tn.T, A_gamma, mat_d_z_d_z, Af_d_z_d_z, Bf_d_z_d_z,
pivots_d_z)
product_matrix_matrix(E_kalman_t[n], mat_d_z_d_z.T, J)
product_matrix_vector(A_gamma, mu_kalman_t[n], vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n + 1]
product_matrix_vector(J, vec_d_z, mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n + 1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z, J.T, mat_d_z_d_z2)
product_matrix_matrix(J, mat_d_z_d_z2, mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
# Compute posterior statistics
product_matrix_matrix(J, E_post_t[n + 1], z_n_z_n_1_post)
outer(mu_post_t[n + 1], mu_post_t[n], mat_d_z_d_z)
z_n_z_n_1_post += mat_d_z_d_z
outer(mu_post_t[n], mu_post_t[n], z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
outer(y_t[n], mu_post_t[n], y_n_z_n_post)
# Update cumulative statistics
z_n_z_n_1_post_sum += z_n_z_n_1_post
z_n_z_n_post_sum += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
gamma_mean_diff += self.compute_gamma(
A, E, z_n_z_n_post, next_z_n_z_n_post, gamma_t[n + 1])
#print gamma_t[n+1]
if get_A_denum == True:
# Compute the denominator of the A update,
# which requires d_z matrices of size (d_z,d_z)
# (i.e. d_z different weighted sums of the z_n_z_n_post matrices)
add(gamma_t[n + 1], E, vec_d_z)
divide(gamma_t[n + 1], vec_d_z, vec_d_z2)
multiply(reshape(z_n_z_n_post, (1, d_z, d_z)),
reshape(vec_d_z2, (d_z, 1, 1)),
weighted_z_n_z_n_post)
A_new_denums += weighted_z_n_z_n_post
have_A_denum = True
new_gamma = (diag(z_n_z_n_post) + 2 * self.gamma_prior_beta
) / (2 * self.gamma_prior_alpha + 3)
gamma_mean_diff += sum((gamma_t[0] - new_gamma)**2) / d_z
gamma_t[0] = new_gamma
gamma_mean_diff /= T_sum
if gamma_mean_diff < self.gamma_change_tolerance:
if training == True:
if have_A_denum == True:
finished = True
self.last_Esteps = Esteps
else:
get_A_denum = True
else:
finished = True
elif gamma_mean_diff <= 10 * self.gamma_change_tolerance and training == True:
get_A_denum = True
if self.verbose:
print gamma_mean_diff, max_Esteps, Esteps
if return_mu_post:
mu_post += [mu_post_t]
# Compute the M step estimates of the parameters
if training == True:
for i in xrange(d_z):
solve(
A_new_denums[i] +
eye(d_z) * self.latent_transition_matrix_regularizer,
z_n_z_n_1_post_sum[i:(i + 1)].T, A_new[i:(i + 1)].T)
solve(
z_n_z_n_post_sum + eye_d_z * self.emission_matrix_regularizer,
y_n_z_n_post_sum.T, C_new.T)
if return_mu_post:
return (A_new, C_new), gamma_set, mu_post
else:
return (A_new, C_new), gamma_set
def EM_step(self,y_set,gamma_set,training = False, return_mu_post = False):
"""
Computes the posterior statistics and outputs the M step
estimates of the parameters.
Also outputs the non-parametric, sparsity inducing variances gamma_t.
Optionally, can output the posterior means of the latent state variables.
"""
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
#V_zero = self.V_zero
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
# Variables for estimating new parameters
A_new = zeros((d_z,d_z))
C_new = zeros((d_y,d_z))
z_n_z_n_1_post_sum = zeros((d_z,d_z))
z_n_z_n_post_sum = zeros((d_z,d_z))
A_new_denums = zeros((d_z,d_z,d_z))
y_n_z_n_post_sum = zeros((d_y,d_z))
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_z2 = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z,d_z))
mat_d_z_d_z2 = zeros((d_z,d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z,d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y,d_y))
A_gamma = zeros((d_z,d_z))
E_gamma = zeros((d_z,d_z))
K = zeros((d_z,d_y))
KC = zeros((d_z,d_z))
J = zeros((d_z,d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y,d_y),order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y,d_z),order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z,d_z),order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z,d_z),order='fortran')
pivots_d_y = zeros((d_y),dtype='i',order='fortran')
pivots_d_z = zeros((d_z),dtype='i',order='fortran')
z_n_z_n_1_post = zeros((d_z,d_z))
z_n_z_n_post = zeros((d_z,d_z))
weighted_z_n_z_n_post = zeros((d_z,d_z,d_z))
next_z_n_z_n_post = zeros((d_z,d_z))
y_n_z_n_post = zeros((d_y,d_z))
if training == True:
max_Esteps = self.max_Esteps
last_Esteps = self.last_Esteps
else:
max_Esteps = self.max_test_Esteps
last_Esteps = self.max_test_Esteps
Esteps = 0
have_A_denum = False
get_A_denum = False
finished = False
while not finished:
T_sum = 0
gamma_mean_diff = 0
z_n_z_n_1_post_sum[:] = 0
z_n_z_n_post_sum[:] = 0
y_n_z_n_post_sum[:] = 0
A_new_denums[:] = 0
Esteps += 1
if Esteps == max_Esteps:
get_A_denum = True
finished = True
elif Esteps >= last_Esteps:
get_A_denum = True
if return_mu_post:
mu_post = []
for y_t,gamma_t in zip(y_set,gamma_set):
T = len(y_t)
T_sum += T
mu_kalman_t = zeros((T,d_z)) # Filtering mus
E_kalman_t = zeros((T,d_z,d_z)) # Filtering Es
mu_post_t = zeros((T,d_z))
E_post_t = zeros((T,d_z,d_z))
P_t = zeros((T-1,d_z,d_z))
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
multiply(C.T,reshape(gamma_t[0],(-1,1)),mat_times_C_trans)
pred[:] = 0
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[0])
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
multiply(mat_d_z_d_z,gamma_t[0],E_kalman_t[0])
# from n=1 to T-1
for n in xrange(T-1):
divide(1.,E,vec_d_z)
divide(1.,gamma_t[n+1],vec_d_z2)
vec_d_z += vec_d_z2
divide(1.,vec_d_z,vec_d_z2)
setdiag(E_gamma,vec_d_z2)
divide(E,gamma_t[n+1],vec_d_z)
vec_d_z += 1
divide(A,reshape(vec_d_z,(-1,1)),A_gamma)
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n],A_gamma.T,mat_d_z_d_z)
product_matrix_matrix(A_gamma,mat_d_z_d_z,P_tn)
P_tn += E_gamma
product_matrix_vector(A_gamma,mu_kalman_t[n],A_times_prev_mu)
product_matrix_matrix(P_tn,C.T,mat_times_C_trans)
product_matrix_vector(C,A_times_prev_mu,pred)
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[n+1]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[n+1])
mu_kalman_t[n+1] += A_times_prev_mu
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z,P_tn,mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n+1] = mat_d_z_d_z2
E_kalman_t[n+1] += mat_d_z_d_z2.T
E_kalman_t[n+1] /= 2
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1],mu_post_t[-1],z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
outer(y_t[-1],mu_post_t[-1],y_n_z_n_post)
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T-2,-1,-1):
next_z_n_z_n_post[:] = z_n_z_n_post
divide(E,gamma_t[n+1],vec_d_z)
vec_d_z += 1
divide(A,reshape(vec_d_z,(-1,1)),A_gamma)
P_tn = P_t[n]
solve(P_tn.T,A_gamma,mat_d_z_d_z,Af_d_z_d_z,Bf_d_z_d_z,pivots_d_z)
product_matrix_matrix(E_kalman_t[n],mat_d_z_d_z.T,J)
product_matrix_vector(A_gamma,mu_kalman_t[n],vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n+1]
product_matrix_vector(J,vec_d_z,mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n+1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z,J.T,mat_d_z_d_z2)
product_matrix_matrix(J,mat_d_z_d_z2,mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
# Compute posterior statistics
product_matrix_matrix(J,E_post_t[n+1],z_n_z_n_1_post)
outer(mu_post_t[n+1],mu_post_t[n],mat_d_z_d_z)
z_n_z_n_1_post += mat_d_z_d_z
outer(mu_post_t[n],mu_post_t[n],z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
outer(y_t[n],mu_post_t[n],y_n_z_n_post)
# Update cumulative statistics
z_n_z_n_1_post_sum += z_n_z_n_1_post
z_n_z_n_post_sum += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
gamma_mean_diff += self.compute_gamma(A,E,z_n_z_n_post,next_z_n_z_n_post,gamma_t[n+1])
#print gamma_t[n+1]
if get_A_denum == True:
# Compute the denominator of the A update,
# which requires d_z matrices of size (d_z,d_z)
# (i.e. d_z different weighted sums of the z_n_z_n_post matrices)
add(gamma_t[n+1],E,vec_d_z)
divide(gamma_t[n+1],vec_d_z,vec_d_z2)
multiply(reshape(z_n_z_n_post,(1,d_z,d_z)),reshape(vec_d_z2,(d_z,1,1)),weighted_z_n_z_n_post)
A_new_denums += weighted_z_n_z_n_post
have_A_denum = True
new_gamma = (diag(z_n_z_n_post)+2*self.gamma_prior_beta)/(2*self.gamma_prior_alpha+3)
gamma_mean_diff += sum((gamma_t[0]-new_gamma)**2)/d_z
gamma_t[0] = new_gamma
gamma_mean_diff /= T_sum
if gamma_mean_diff < self.gamma_change_tolerance:
if training == True:
if have_A_denum == True:
finished = True
self.last_Esteps = Esteps
else:
get_A_denum = True
else:
finished = True
elif gamma_mean_diff <= 10*self.gamma_change_tolerance and training == True:
get_A_denum = True
if self.verbose:
print gamma_mean_diff, max_Esteps, Esteps
if return_mu_post:
mu_post += [mu_post_t]
# Compute the M step estimates of the parameters
if training == True:
for i in xrange(d_z):
solve(A_new_denums[i]+eye(d_z)*self.latent_transition_matrix_regularizer,z_n_z_n_1_post_sum[i:(i+1)].T,A_new[i:(i+1)].T)
solve(z_n_z_n_post_sum+eye_d_z*self.emission_matrix_regularizer,y_n_z_n_post_sum.T,C_new.T)
if return_mu_post:
return (A_new,C_new),gamma_set,mu_post
else:
return (A_new,C_new),gamma_set
